Data analytics models are typically highly tuned and customized for a particular application. Such tuning and customization often requires pre-existing knowledge about the particular application, and can require the use of complex manual tools to achieve this tuning and customization. For example, an expert in a certain field may carefully tune and customize an analytics model for use in the expert's field using a manual tool.
While a highly tuned, expert customized analytics model may be useful for a particular application or field, because of the high level of tuning and customization, the analytics model is typically useless or at least inaccurate for other applications and fields. Conversely, a general purpose analytics framework typically is not specialized enough for most applications without substantial customization.